Parameterization for fading compensation

ABSTRACT

Techniques and tools for performing fading compensation in video processing applications are described. For example, during encoding, a video encoder performs fading compensation using fading parameters comprising a scaling parameter and a shifting parameter on one or more reference images. During decoding, a video decoder performs corresponding fading compensation on the one or more reference images.

RELATED APPLICATION INFORMATION

This application is a continuation of U.S. patent application Ser. No.13/584,671, filed Aug. 13, 2012, entitled “PARAMETERIZATION FOR FADINGCOMPENSATION,” which is a continuation of U.S. patent application Ser.No. 11/838,758, filed Aug. 14, 2007, entitled “PARAMETERIZATION FORFADING COMPENSATION,” which is a continuation of U.S. patent applicationSer. No. 10/378,958, filed Mar. 3, 2003, entitled “PARAMETERIZATION FORFADING COMPENSATION,” which claims the benefit of U.S. ProvisionalPatent Application Ser. No. 60/377,628, filed May 3, 2002, thedisclosure of which is incorporated herein by reference. Thisapplication relates to U.S. patent application Ser. No. 10/378,988,entitled “Fading Estimation/Compensation,” filed Mar. 3, 2003, and U.S.patent application Ser. No. 10/379,079, entitled “Signaling for FadingCompensation,” filed Mar. 3, 2003, the disclosures of which areincorporated herein by reference.

TECHNICAL FIELD

Techniques and tools for parameterization for fading compensation invideo processing applications are described. For example, a videoencoder performs fading compensation on a reference image based onfading parameters.

BACKGROUND

Digital video consumes large amounts of storage and transmissioncapacity. A typical raw digital video sequence includes 15 or 30 framesper second. Each frame can include tens or hundreds of thousands ofpixels (also called pels). Each pixel represents a tiny element of thepicture. In raw form, a computer commonly represents a pixel with 24bits. Thus, the number of bits per second, or bit rate, of a typical rawdigital video sequence can be 5 million bits/second or more.

Most computers and computer networks lack the resources to process rawdigital video. For this reason, engineers use compression (also calledcoding or encoding) to reduce the bit rate of digital video. Compressioncan be lossless, in which quality of the video does not suffer butdecreases in bit rate are limited by the complexity of the video. Or,compression can be lossy, in which quality of the video suffers butdecreases in bit rate are more dramatic. Decompression reversescompression.

In general, video compression techniques include intraframe compressionand interframe compression. Intraframe compression techniques compressindividual frames, typically called I-frames or key frames. Interframecompression techniques compress frames with reference to precedingand/or following frames, which are typically called predicted frames,P-frames, or B-frames.

Microsoft Corporation's Windows Media Video, Version 8 [“WMV8”] includesa video encoder and a video decoder. The WMV8 encoder uses intraframeand interframe compression, and the WMV8 decoder uses intraframe andinterframe decompression.

A. Intraframe Compression in WMV8

FIG. 1 shows an example of block-based intraframe compression (100) of ablock (105) of pixels in a key frame in the WMV8 encoder. For example,the WMV8 encoder splits a key video frame into 8×8 blocks of pixels andapplies an 8×8 Discrete Cosine Transform [“DCT”] (110) to individualblocks, converting the 8×8 block of pixels (105) into an 8×8 block ofDCT coefficients (115). The encoder quantizes (120) the DCTcoefficients, resulting in an 8×8 block of quantized DCT coefficients(125) which the encoder then prepares for entropy encoding.

The encoder encodes the DC coefficient (126) as a differential from theDC coefficient (136) of a previously encoded neighbor (e.g., neighborblock (135)) of the block being encoded. The encoder entropy encodes thedifferential (140). FIG. 1 shows the left column (127) of ACcoefficients encoded as a differential (147) from the left column (137)of the neighboring (to the left) block (135). The remaining ACcoefficients are from the block (125) of quantized DCT coefficients.

The encoder scans (150) the 8×8 block (145) of predicted, quantized ACDCT coefficients into a one-dimensional array (155) and then entropyencodes the scanned AC coefficients using a variation of run lengthcoding (160). The encoder selects an entropy code from one or morerun/level/last tables (165) and outputs the entropy code (170).

B. Interframe Compression in WMV8

Interframe compression in the WMV8 encoder uses block-based motioncompensated prediction coding followed by transform coding of theresidual error. FIGS. 2 and 3 illustrate the block-based interframecompression for a predicted frame in the WMV8 encoder. In particular,FIG. 2 illustrates motion estimation for a predicted frame (210) andFIG. 3 illustrates compression of a prediction residual for amotion-estimated block of a predicted frame.

For example, the WMV8 encoder splits a predicted frame into 8×8 blocksof pixels. Groups of four 8×8 blocks form macroblocks. For eachmacroblock, a motion estimation process is performed. The motionestimation approximates the motion of the macroblock of pixels relativeto a reference frame, for example, a previously coded, preceding frame.In FIG. 2, the WMV8 encoder computes a motion vector for a macroblock(215) in the predicted frame (210). To compute the motion vector, theencoder searches in a search area (235) of a reference frame (230).Within the search area (235), the encoder compares the macroblock (215)from the predicted frame (210) to various candidate macroblocks in orderto find a candidate macroblock that is a good match. After the encoderfinds a good matching macroblock, the encoder outputs informationspecifying the motion vector (entropy coded) for the matching macroblockso the decoder can find the matching macroblock during decoding. Whendecoding the predicted frame (210) with motion compensation, a decoderuses the motion vector to compute a prediction macroblock for themacroblock (215) using information from the reference frame (230). Theprediction for the macroblock (215) is rarely perfect, so the encoderusually encodes 8×8 blocks of pixel differences (also called the erroror residual blocks) between the prediction macroblock and the macroblock(215) itself.

FIG. 3 illustrates an example of computation and encoding of an errorblock (335) in the WMV8 encoder. The error block (335) is the differencebetween the predicted block (315) and the original current block (325).The encoder applies a DCT (340) to the error block (335), resulting inan 8×8 block (345) of coefficients. The encoder then quantizes (350) theDCT coefficients, resulting in an 8×8 block of quantized DCTcoefficients (355). The quantization step size is adjustable.Quantization results in loss of precision, but not complete loss of theinformation for the coefficients.

The encoder then prepares the 8×8 block (355) of quantized DCTcoefficients for entropy encoding. The encoder scans (360) the 8×8 block(355) into a one dimensional array (365) with 64 elements, such thatcoefficients are generally ordered from lowest frequency to highestfrequency, which typically creates long runs of zero values.

The encoder entropy encodes the scanned coefficients using a variationof run length coding (370). The encoder selects an entropy code from oneor more run/level/last tables (375) and outputs the entropy code.

FIG. 4 shows an example of a corresponding decoding process (400) for aninter-coded block. Due to the quantization of the DCT coefficients, thereconstructed block (475) is not identical to the corresponding originalblock. The compression is lossy.

In summary of FIG. 4, a decoder decodes (410, 420) entropy-codedinformation representing a prediction residual using variable lengthdecoding (410) with one or more run/level/last tables (415) and runlength decoding (420). The decoder inverse scans (430) a one-dimensionalarray (425) storing the entropy-decoded information into atwo-dimensional block (435). The decoder inverse quantizes and inversediscrete cosine transforms (together, 440) the data, resulting in areconstructed error block (445). In a separate motion compensation path,the decoder computes a predicted block (465) using motion vectorinformation (455) for displacement from a reference frame. The decodercombines (470) the predicted block (465) with the reconstructed errorblock (445) to form the reconstructed block (475).

The amount of change between the original and reconstructed frame istermed the distortion and the number of bits required to code the frameis termed the rate for the frame. The amount of distortion is roughlyinversely proportional to the rate. In other words, coding a frame withfewer bits (greater compression) will result in greater distortion, andvice versa.

C. Limitations of Conventional Motion-Based Video Compression

Video sequences with effects such as fading, morphing, and blendingrequire relatively large amounts of bits to encode because conventionalmotion-based video compression methods are generally not effective onsuch frames. For example, consider a video sequence in which an objectin a frame has moved slightly in one direction from one frame to thenext. In a typical block-matching motion estimation technique, it may bea simple matter in a video sequence without fading to find a good matchin the previous frame for a block in the current frame and encode theresulting motion vector. However, if, for example, a “fade-to-black” isoccurring in the video sequence, every luminance value in the currentframe may have changed relative to the previous frame, preventing thevideo encoder from finding a good match for the block. Fading also mayoccur in a sequence due to natural illumination changes. Blending andmorphing, which are other transitioning effects, may also reduce theeffectiveness of straightforward motion estimation/compensation.

D. Standards for Video Compression and Decompression

Aside from WMV8, several international standards relate to videocompression and decompression. These standards include the MotionPicture Experts Group [“MPEG”] 1, 2, and 4 standards and the H.261,H.262, and H.263 standards from the International TelecommunicationUnion [“ITU”]. Like WMV8, these standards use a combination ofintraframe and interframe compression, although the standards typicallydiffer from WMV8 in the details of the compression techniques used. Forexample, Annex P of the H.263 standard describes a Reference PictureResampling mode for use in prediction that can be used to adaptivelyalter the resolution of pictures during encoding.

Given the critical importance of video compression and decompression todigital video, it is not surprising that video compression anddecompression are richly developed fields. Whatever the benefits ofprevious video compression and decompression techniques, however, theydo not have the advantages of the following techniques and tools.

SUMMARY

One of the goals of video compression is to improve rate-distortionperformance—in other words, to achieve the same quality using fewerbits, or to use the same amount of bits but achieve higher quality. Oneway to achieve this goal is to identify portions of video sequences thatrequire relatively large amounts of bits to encode, and then find waysto better compress such portions. Portions of video sequences witheffects such as fading, morphing, and blending are infrequent, butrequire relatively large amounts of bits to encode when they do occurbecause conventional motion-based video compression methods aregenerally not effective on such portions.

Accordingly, in summary, the detailed description is directed to varioustechniques and tools for improving rate-distortion performance for videosequences that include fade-ins, fade-outs, cross-fades, or otherfading, blending, or morphing effects. For example, a video encoderperforms fading compensation for a current frame by adjusting areference frame based on fading parameters. This makes motioncompensation using the reference frame more efficient. A video decoderperforms the fading compensation by adjusting the reference frame. Inparticular, the detailed description addresses efficient ways toparameterize the reference frame adjustment.

In a first set of techniques and tools, a video encoder or decoderobtains fading parameters comprising a scaling parameter and a shiftingparameter. The video encoder or decoder performs fading compensation byremapping pixel values (such as chrominance or luminance values) in areference video image (such as a frame, field, or object plane) in termsof the fading parameters, which may be coded, for example, using fixed-or variable-length codes. In some cases, the scaling parameter is acontrast parameter and the shifting parameter is a brightness parameter.The fading parameters specify, for example, a pixel-wise lineartransform comprising scaling pixel values (such as chrominance orluminance values) using the scaling parameter. In some cases, thepixel-wise linear transform further comprises shifting scaled pixelvalues using the shifting parameter. The video encoder or decoderperforms motion compensation for at least part of a current video imagerelative to the remapped reference video image.

In a second set of techniques and tools, a video encoder or decoderobtains global luminance change compensation parameters consisting of ascaling parameter and a shifting parameter. The video encoder or decoderperforms global luminance change compensation by remapping pixel valuesfor all of a reference video frame in terms of the global luminancechange compensation parameters. The video encoder or decoder performsmotion compensation for at least part of a current video frame relativeto the remapped reference video frame.

The various techniques and tools can be used in combination orindependently. Different embodiments implement one or more of thedescribed techniques and tools.

Additional features and advantages will be made apparent from thefollowing detailed description of different embodiments that proceedswith reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing block-based intraframe compression accordingto the prior art.

FIG. 2 is a diagram showing motion estimation in a video encoderaccording to the prior art.

FIG. 3 is a diagram showing block-based interframe compression accordingto the prior art.

FIG. 4 is a diagram showing block-based interframe decompressionaccording to the prior art.

FIG. 5 is a block diagram of a suitable computing environment in whichseveral described embodiments may be implemented.

FIG. 6 is a block diagram of a generalized video encoder system used inseveral described embodiments.

FIG. 7 is a block diagram of a generalized video decoder system used inseveral described embodiments.

FIG. 8 is a flowchart showing a technique for encoding video usingfading estimation and compensation.

FIG. 9 is a flowchart showing a technique for decoding video encodedusing fading compensation.

FIG. 10 is a block diagram of a video encoder system capable ofperforming fading estimation and compensation.

FIG. 11 is a block diagram of a video decoder system capable ofperforming fading compensation.

FIG. 12 is a flowchart showing a technique for fading detection andparameter extraction.

FIG. 13 is a chart showing a signaling scheme for indicating whether touse fading compensation for a frame.

DETAILED DESCRIPTION

Described embodiments relate to techniques and tools for fadingestimation and/or compensation. Without fading compensation/estimation,video sequences with effects such as fading, morphing, and blendingrequire relatively large amounts of bits to encode because conventionalmotion-based video compression methods are generally not effective onsuch frames. Described embodiments improve rate-distortion performanceby performing fading estimation/compensation in such sequences. Variousembodiments relate to techniques and tools for estimating, applying,coding and/or decoding global luminance change parameters.

Fading compensation in some embodiments involves performing a globalluminance change to one or more reference frames to compensate forfading. A global luminance change is a luminance change across a definedregion, which may be a frame, a part of a frame (e.g., an individualblock or macroblock in a frame, or a group of macroblocks in a frame),or another specific portion of an image being coded or decoded. Acurrent frame is then predicted by motion estimation/compensation fromthe adjusted one or more reference frames. Alternatively, fadingcompensation involves a global change to a reference frame to compensatefor effects such as blending or morphing. Generally, fading compensationincludes any compensation for fading (i.e., fade-to-black orfade-from-black), blending, morphing, or other natural or syntheticlighting effects that affect pixel value intensities. Without loss ofgenerality, however, the terms global luminance change and fading areused interchangeably herein, unless the context clearly shows otherwise.

As an alternative to performing fading compensation on frames, someembodiments perform fading compensation on fields, object layers orother images.

In some embodiments, fading compensation occurs by adjusting pixelvalues in the luminance and chrominance channels of a reference frame inYUV color space. The adjustment includes scaling and shifting luminancepixel values and scaling and shifting chrominance pixel values.Alternatively, the color space is different (e.g., YIQ or RGB) and/orthe compensation uses other adjustment techniques.

An encoder/decoder performs fading estimation/compensation on aframe-by-frame basis. Alternatively, an encoder/decoder performs fadingestimation/compensation on some other basis or on a portion of a framesuch as one or more blocks or macroblocks.

The various techniques and tools can be used in combination orindependently. Different embodiments implement one or more of thedescribed techniques and tools. Although the operations for thesetechniques are typically described in a particular, sequential order forthe sake of presentation, it should be understood that this manner ofdescription encompasses minor rearrangements in the order of operations,unless a particular ordering is required. For example, operationsdescribed sequentially may in some cases be rearranged or performedconcurrently. Moreover, for the sake of simplicity, flowcharts typicallydo not show the various ways in which particular techniques can be usedin conjunction with other techniques.

In some embodiments, the video encoder and decoder use various flags andsignals in a bitstream. While specific flags and signals are described,it should be understood that this manner of description encompassesdifferent conventions (e.g., 0's rather than 1's) for the flags andsignals.

I. Computing Environment

FIG. 5 illustrates a generalized example of a suitable computingenvironment (500) in which several of the described embodiments may beimplemented. The computing environment (500) is not intended to suggestany limitation as to scope of use or functionality, as the techniquesand tools may be implemented in diverse general-purpose orspecial-purpose computing environments.

With reference to FIG. 5, the computing environment (500) includes atleast one processing unit (510) and memory (520). In FIG. 5, this mostbasic configuration (530) is included within a dashed line. Theprocessing unit (510) executes computer-executable instructions and maybe a real or a virtual processor. In a multi-processing system, multipleprocessing units execute computer-executable instructions to increaseprocessing power. The memory (520) may be volatile memory (e.g.,registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flashmemory, etc.), or some combination of the two. The memory (520) storessoftware (580) implementing an encoder or decoder, such as a videoencoder or decoder.

A computing environment may have additional features. For example, thecomputing environment (500) includes storage (540), one or more inputdevices (550), one or more output devices (560), and one or morecommunication connections (570). An interconnection mechanism (notshown) such as a bus, controller, or network interconnects thecomponents of the computing environment (500). Typically, operatingsystem software (not shown) provides an operating environment for othersoftware executing in the computing environment (500), and coordinatesactivities of the components of the computing environment (500).

The storage (540) may be removable or non-removable, and includesmagnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any othermedium which can be used to store information and which can be accessedwithin the computing environment (500). The storage (540) storesinstructions for the software (580) implementing the encoder or decoder.

The input device(s) (550) may be a touch input device such as akeyboard, mouse, pen, or trackball, a voice input device, a scanningdevice, or another device that provides input to the computingenvironment (500). For audio or video encoding, the input device(s)(550) may be a sound card, video card, TV tuner card, or similar devicethat accepts audio or video input in analog or digital form, or a CD-ROMor CD-RW that reads audio or video samples into the computingenvironment (500). The output device(s) (560) may be a display, printer,speaker, CD-writer, or another device that provides output from thecomputing environment (500).

The communication connection(s) (570) enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia include wired or wireless techniques implemented with anelectrical, optical, RF, infrared, acoustic, or other carrier.

The techniques and tools can be described in the general context ofcomputer-readable media. Computer-readable media are any available mediathat can be accessed within a computing environment. By way of example,and not limitation, with the computing environment (500),computer-readable media include memory (520), storage (540),communication media, and combinations of any of the above.

The techniques and tools can be described in the general context ofcomputer-executable instructions, such as those included in programmodules, being executed in a computing environment on a target real orvirtual processor. Generally, program modules include routines,programs, libraries, objects, classes, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes. The functionality of the program modules may be combined or splitbetween program modules as desired in various embodiments.Computer-executable instructions for program modules may be executedwithin a local or distributed computing environment.

For the sake of presentation, the detailed description uses terms like“estimate,” “signal,” “encode,” and “decode” to describe computeroperations in a computing environment. These terms are high-levelabstractions for operations performed by a computer, and should not beconfused with acts performed by a human being. The actual computeroperations corresponding to these terms vary depending onimplementation.

II. Generalized Video Encoder and Decoder

FIG. 6 is a block diagram of a generalized video encoder (600) and FIG.7 is a block diagram of a generalized video decoder (700).

The relationships shown between modules within the encoder and decoderindicate the main flow of information in the encoder and decoder; otherrelationships are not shown for the sake of simplicity. In particular,FIGS. 6 and 7 usually do not show side information indicating theencoder settings, modes, tables, etc. used for a video sequence, frame,macroblock, block, etc. Such side information is sent in the outputbitstream, typically after entropy encoding of the side information. Theformat of the output bitstream can be a Windows Media Video format oranother format.

The encoder (600) and decoder (700) are block-based and use a 4:2:0macroblock format with each macroblock including 4 luminance 8×8luminance blocks (at times treated as one 16×16 macroblock) and two 8×8chrominance blocks. Alternatively, the encoder (600) and decoder (700)are object-based, use a different macroblock or block format, or performoperations on sets of pixels of different size or configuration than 8×8blocks and 16×16 macroblocks.

Depending on implementation and the type of compression desired, modulesof the encoder or decoder can be added, omitted, split into multiplemodules, combined with other modules, and/or replaced with like modules.In alternative embodiments, encoder or decoders with different modulesand/or other configurations of modules perform one or more of thedescribed techniques.

A. Video Encoder

FIG. 6 is a block diagram of a general video encoder system (600). Theencoder system (600) receives a sequence of video frames including acurrent frame (605), and produces compressed video information (695) asoutput. Particular embodiments of video encoders typically use avariation or supplemented version of the generalized encoder (600).

The encoder system (600) compresses predicted frames and key frames. Forthe sake of presentation, FIG. 6 shows a path for key frames through theencoder system (600) and a path for forward-predicted frames. Many ofthe components of the encoder system (600) are used for compressing bothkey frames and predicted frames. The exact operations performed by thosecomponents can vary depending on the type of information beingcompressed.

A predicted frame [also called p-frame, b-frame for bi-directionalprediction, or inter-coded frame] is represented in terms of prediction(or difference) from one or more other frames. A prediction residual isthe difference between what was predicted and the original frame. Incontrast, a key frame [also called i-frame, intra-coded frame] iscompressed without reference to other frames.

If the current frame (605) is a forward-predicted frame, a motionestimator (610) estimates motion of macroblocks or other sets of pixelsof the current frame (605) with respect to a reference frame, which isthe reconstructed previous frame (625) buffered in the frame store(620). In alternative embodiments, the reference frame is a later frameor the current frame is bi-directionally predicted. The motion estimator(610) outputs as side information motion information (615) such asmotion vectors. A motion compensator (630) applies the motioninformation (615) to the reconstructed previous frame (625) to form amotion-compensated current frame (635). The prediction is rarelyperfect, however, and the difference between the motion-compensatedcurrent frame (635) and the original current frame (605) is theprediction residual (645). Alternatively, a motion estimator and motioncompensator apply another type of motion estimation/compensation.

A frequency transformer (660) converts the spatial domain videoinformation into frequency domain (i.e., spectral) data. For block-basedvideo frames, the frequency transformer (660) applies a discrete cosinetransform [“DCT”] or variant of DCT to blocks of the motion predictionresidual data, producing blocks of DCT coefficients. Alternatively, thefrequency transformer (660) applies another conventional frequencytransform such as a Fourier transform or uses wavelet or subbandanalysis. In some embodiments, the frequency transformer (660) applies afrequency transform to blocks of spatial prediction residuals for keyframes. The frequency transformer (660) can apply an 8×8, 8×4, 4×8, orother size frequency transforms.

A quantizer (670) then quantizes the blocks of spectral datacoefficients. The quantizer applies uniform, scalar quantization to thespectral data with a step-size that varies on a frame-by-frame basis orother basis. Alternatively, the quantizer applies another type ofquantization to the spectral data coefficients, for example, anon-uniform, vector, or non-adaptive quantization, or directly quantizesspatial domain data in an encoder system that does not use frequencytransformations. In addition to adaptive quantization, the encoder (600)can use frame dropping, adaptive filtering, or other techniques for ratecontrol.

When a reconstructed current frame is needed for subsequent motionestimation/compensation, an inverse quantizer (676) performs inversequantization on the quantized spectral data coefficients. An inversefrequency transformer (666) then performs the inverse of the operationsof the frequency transformer (660), producing a reconstructed predictionresidual (for a predicted frame) or a reconstructed key frame. If thecurrent frame (605) was a key frame, the reconstructed key frame istaken as the reconstructed current frame (not shown). If the currentframe (605) was a predicted frame, the reconstructed prediction residualis added to the motion-compensated current frame (635) to form thereconstructed current frame. The frame store (620) buffers thereconstructed current frame for use in predicting the next frame. Insome embodiments, the encoder applies a deblocking filter to thereconstructed frame to adaptively smooth discontinuities in the blocksof the frame.

The entropy coder (680) compresses the output of the quantizer (670) aswell as certain side information (e.g., motion information (615),quantization step size). Typical entropy coding techniques includearithmetic coding, differential coding, Huffman coding, run lengthcoding, LZ coding, dictionary coding, and combinations of the above. Theentropy coder (680) typically uses different coding techniques fordifferent kinds of information (e.g., DC coefficients, AC coefficients,different kinds of side information), and can choose from among multiplecode tables within a particular coding technique.

The entropy coder (680) puts compressed video information (695) in thebuffer (690). A buffer level indicator is fed back to bitrate adaptivemodules. The compressed video information (695) is depleted from thebuffer (690) at a constant or relatively constant bitrate and stored forsubsequent streaming at that bitrate. Alternatively, the encoder system(600) streams compressed video information immediately followingcompression.

Before or after the buffer (690), the compressed video information (695)can be channel coded for transmission over the network. The channelcoding can apply error detection and correction data to the compressedvideo information (695).

B. Video Decoder

FIG. 7 is a block diagram of a general video decoder system (700). Thedecoder system (700) receives information (795) for a compressedsequence of video frames and produces output including a reconstructedframe (705). Particular embodiments of video decoders typically use avariation or supplemented version of the generalized decoder (700).

The decoder system (700) decompresses predicted frames and key frames.For the sake of presentation, FIG. 7 shows a path for key frames throughthe decoder system (700) and a path for forward-predicted frames. Manyof the components of the decoder system (700) are used for decompressingboth key frames and predicted frames. The exact operations performed bythose components can vary depending on the type of information beingcompressed.

A buffer (790) receives the information (795) for the compressed videosequence and makes the received information available to the entropydecoder (780). The buffer (790) typically receives the information at arate that is fairly constant over time, and includes a jitter buffer tosmooth short-term variations in bandwidth or transmission. The buffer(790) can include a playback buffer and other buffers as well.Alternatively, the buffer (790) receives information at a varying rate.Before or after the buffer (790), the compressed video information canbe channel decoded and processed for error detection and correction.

The entropy decoder (780) entropy decodes entropy-coded quantized dataas well as entropy-coded side information (e.g., motion information(715), quantization step size), typically applying the inverse of theentropy encoding performed in the encoder. Entropy decoding techniquesinclude arithmetic decoding, differential decoding, Huffman decoding,run length decoding, LZ decoding, dictionary decoding, and combinationsof the above. The entropy decoder (780) frequently uses differentdecoding techniques for different kinds of information (e.g., DCcoefficients, AC coefficients, different kinds of side information), andcan choose from among multiple code tables within a particular decodingtechnique.

If the frame (705) to be reconstructed is a forward-predicted frame, amotion compensator (730) applies motion information (715) to a referenceframe (725) to form a prediction (735) of the frame (705) beingreconstructed. For example, the motion compensator (730) uses amacroblock motion vector to find a macroblock in the reference frame(725). A frame buffer (720) stores previous reconstructed frames for useas reference frames. Alternatively, a motion compensator applies anothertype of motion compensation. The prediction by the motion compensator israrely perfect, so the decoder (700) also reconstructs predictionresiduals.

When the decoder needs a reconstructed frame for subsequent motioncompensation, the frame store (720) buffers the reconstructed frame foruse in predicting the next frame. In some embodiments, the encoderapplies a deblocking filter to the reconstructed frame to adaptivelysmooth discontinuities in the blocks of the frame.

An inverse quantizer (770) inverse quantizes entropy-decoded data. Ingeneral, the inverse quantizer applies uniform, scalar inversequantization to the entropy-decoded data with a step-size that varies ona frame-by-frame basis or other basis. Alternatively, the inversequantizer applies another type of inverse quantization to the data, forexample, a non-uniform, vector, or non-adaptive quantization, ordirectly inverse quantizes spatial domain data in a decoder system thatdoes not use inverse frequency transformations.

An inverse frequency transformer (760) converts the quantized, frequencydomain data into spatial domain video information. For block-based videoframes, the inverse frequency transformer (760) applies an inverse DCT[“IDCT”] or variant of IDCT to blocks of the DCT coefficients, producingmotion prediction residual data. Alternatively, the frequencytransformer (760) applies another conventional inverse frequencytransform such as a Fourier transform or uses wavelet or subbandsynthesis. In some embodiments, the inverse frequency transformer (760)applies an inverse frequency transform to blocks of spatial predictionresiduals for key frames. The inverse frequency transformer (760) canapply an 8×8, 8×4, 4×8, or other size inverse frequency transforms.

III. Global Luminance Change Estimation/Compensation

Some described embodiments involve techniques and tools for estimating,applying, coding and/or decoding global luminance changes described by asmall number of parameters. The tools and techniques allow the samesubjective and objective quality of reconstructed video to be achievedat a lower bit rate. Each individual tool or technique implements one ormore of 1) a global luminance change compensation scheme; 2)parameterization of global luminance parameters; 3) computationallyefficient estimation/extraction of global luminance parameters; 4)low-cost signaling of frames with global luminance change; and 5)low-cost signaling of global luminance parameters. The luminance changemay be global to a frame, a field, a part of a frame/field such as anindividual block, individual macroblock, or group of macroblocks, oranother specific portion of an image. While much of the followingdescription addresses parameterization and compensation for luminancechange in an entire frame, the same framework for luminance changecompensation can be used to (a) determine presence of fading in aportion (e.g., an individual block or macroblock) of an image, (b)compute fading parameters within this portion, and (c) parameterize andtransmit these fading parameters for the given portion of the image.These techniques can be repeated for multiple portions of the image.

A global luminance change (also known as “fading”) may be a change inthe brightness and/or contrast of the scene. Typically, the change islinear, but fading can also be defined as including any smooth,nonlinear mapping within the same framework. Fading, morphing andblending are widely used in creation of video content for smoothing theevolution of video frames across scene transitions and for providingspecial effects. Further, certain sequences exhibit fading naturally dueto changes in illumination. Video frames with effects such as fading,morphing, and blending require relatively large amounts of bits toencode with conventional motion-based video compression methods, whichare generally not effective on such frames.

A. Global Luminance Change Compensation Scheme

FIG. 8 shows a technique (800) for encoding video using global luminancechange compensation. An encoder such as the encoder (600) shown in FIG.6 may perform the technique (800).

First, the encoder checks for fading (810) in a frame to be encoded,such as the current frame being encoded in a video sequence. If theencoder detects fading (810) for the frame, the encoder obtains fadingparameters (820). For example, the encoder detects fading and obtainsfading parameters as described below in Section C. Alternatively, theencoder uses a different technique to detect fading and/or obtainparameters. The encoder signals whether fading compensation is on oroff, and, if on, signals the fading parameters as well.

If fading was detected for the frame, the encoder then performs fadingcompensation on one or more reference frames for the frame (830), forexample, as described below in Section B. When the encoder performsfading compensation on multiple reference frames, the multiple referenceframes may be before or after (in playback order) the frame beingencoded. The encoder signals which portions of the frame being encodedare compensated from which of the multiple reference frames. For thissignaling, the encoder may use signaling already used for referenceframe selection in such systems. In some embodiments, the encoder mayuse both an original reference frame and a remapped reference frame inmotion estimation/compensation for the frame to be encoded. The encodermay do this, for example, to encode a current frame that has both fadedcontent and unfaded overlays.

After fading compensation, the encoder encodes the frame (840) usingmotion estimation/compensation from the adjusted reference frame(s). Ifthe encoder does not detect fading, the encoder encodes the frame (840)without obtaining fading parameters or performing fading compensation.When encoding is done (850), the process ends.

FIG. 9 shows a technique (900) for decoding video encoded using fadingcompensation. A decoder such as the decoder (700) shown in FIG. 7 mayperform the technique (900).

First, the decoder checks (910) whether fading is on or off for theframe to be decoded. One way to perform this checking is to checksignaling information sent by an encoder. If the decoder determines thatfading is on (910) for the frame, the decoder performs fadingcompensation (920). For example, the decoder gets fading parameters sentby the encoder and performs fading compensation (as done in the encoder)on one or more reference frames for the frame to be decoded.

The decoder then decodes the frame (930), for example, using motioncompensation from the adjusted reference frame(s). If fading is off, thedecoder decodes the frame (930) without performing fading compensation.When decoding is done (940), the process ends.

FIG. 10 shows an exemplary encoder framework (1000) for performingglobal luminance change compensation. In this framework (1000), theencoder conditionally remaps a reference frame using parameters obtainedby fading estimation. The encoder performs remapping, or fadingcompensation, when the encoder detects fading with a good degree ofcertainty and consistency across the frame. Otherwise, fadingcompensation is an identity operation (i.e., output=input).

Referring to FIG. 10, the encoder compares a current frame (1010) with areference frame (1020) using a fading detection module (1030) todetermine whether fading is occurring. In some embodiments, thereference frame is the frame previous to the current frame in a videosequence. Alternatively, the reference frame is earlier than theprevious frame or after the current frame. When multiple referenceframes are used, the encoder may check for fading in each referenceframe. The encoder produces a “fading on” or “fading off” signal (1040)based on the results of the fading detection.

If fading is on, the fading estimation module (1050) estimates fadingparameters (1060) based on calculations performed on the current frame(1010) and the reference frame (1020). (Fading estimation details insome embodiments are discussed below in Section C.)

The fading compensation module (1070) uses the fading parameters (1060)to remap the reference frame (1020) (alternatively, multiple referenceframes). The encoder can then use other encoder modules (1080) (e.g.,motion estimation and compensation, frequency transformer, andquantization modules) to compress the frame. The encoder outputs motionvectors, residuals and other information (1090) that define the encodedcurrent frame (1010). Aside from motion estimation/compensation withtranslational motion vectors, the framework for global luminance changecompensation is applicable across a wide variety of motioncompensation-based video codecs.

FIG. 11 shows an exemplary decoder framework (1100) for performingglobal luminance change compensation. The decoder produces a decodedcurrent frame (1110). To decode an encoded fading-compensated frame, thedecoder performs fading compensation on a previously decoded referenceframe (1120) (alternatively, multiple reference frames) using a fadingcompensation module (1130).

The decoder performs fading compensation on the reference frame (1120)if the fading on/off signal (1140) indicates that fading is on for theencoded current frame (1110). The decoder performs fading compensation(as done in the encoder) by using the fading parameters (1150) obtainedduring fading estimation. Otherwise (if fading is off for the currentframe), fading compensation is an identity operation (i.e.,output=input).

The decoder can then use other decoder modules (1160) (e.g., motioncompensation, inverse frequency transformer, and inverse quantizationmodules) to decompress the encoded frame using motion vectors, residualsand other information (1170) provided by the encoder.

B. Parameterization and Compensation

In video editing, synthetic fading is sometimes realized by applying asimple, pixel-wise linear transform to the luminance and chrominancechannels. Likewise, cross-fading is sometimes realized as linear sums oftwo video sequences, with the composition changing over time.Accordingly, in some embodiments, an encoder such as one shown in theframework (1000) of FIG. 10 parameterizes fading (whether natural orsynthetic) as a pixel-wise linear transform and parameterizescross-fading as a linear sum, and a decoder such as one shown in theframework (1100) of FIG. 11 performs corresponding transforms.

Let I(n) be an image at frame n and I(n−1) be an image at the previousframe. Where motion is small, simple fading is modeled by the firstorder relationship in Equation 1. (The relation in Equation 1 isapproximate because of possible motion in the video sequence.)

I(n)≈CI(n−1)+B  (1),

where the fading parameters B and C correspond to brightness andcontrast, respectively. When nonlinear fading occurs, the first ordercomponent typically accounts for the bulk of the change.

Cross-fades from an image sequence U(n) to an image sequence V(n) can bemodeled by the relationship in Equation 2. The relation in Equation 2 isapproximate because of possible motion in the sequences.

$\begin{matrix}{\begin{matrix}{{I(n)} \approx {{\alpha \; {nV}} + {\left( {1 - {\alpha \; n}} \right)U}}} \\{\approx {{I\left( {n - 1} \right)} + {\alpha \left( {V - U} \right)}}} \\{\approx \left\{ \begin{matrix}{\left( {1 - \alpha} \right){I\left( {n - 1} \right)}} & {n \approx 0} \\{\left( {1 + \alpha} \right){I\left( {n - 1} \right)}} & {n \approx {1/\alpha}}\end{matrix} \right.}\end{matrix}\quad} & (2)\end{matrix}$

n≈0 represents the beginning of the cross-fade, and n≈1/α represents theend of the cross-fade. For cross-fades spanning several frames, α issmall. At the start of the cross-fade, the nth frame is close to anattenuated (contrast<1) version of the n−1th frame. Towards the end, thenth frame is an amplified (contrast>1) version of the n−1th frame. Inother words, at the beginning of the cross-fade, the nth frame can bemodeled as the n−1th frame scaled by the contrast value 1−α, while atthe end of the cross-fade, the nth frame can be modeled as the n−1thframe scaled by the contrast value 1+α. Equation 2 shows that at thebeginning and end of the cross-fade, the encoder can obtain the nthframe by remapping a reference frame (e.g., the n−1th frame) using alinear rule (such as those shown in Equations 3 and 4 below).

The encoder carries out compensation of global luminance change byremapping a reference frame. The encoder remaps the reference frame on apixel-by-pixel basis, or on some other basis. The original, un-remappedreference frame is essentially discarded (although in a multiplereference frame scenario, the un-remapped reference may be used aswell).

The following linear rule, based on Equation 1, remaps the luminancevalues of the reference frame R to the remapped reference frame{circumflex over (R)} in terms of the two parameters B and C(corresponding to brightness and contrast of the scene):

{circumflex over (R)}≈CR+B  (3)

The luminance values of the reference frame are scaled (or, “weighted”)by the contrast value and shifted (i.e., by adding an offset) by thebrightness value. For chrominance, the remapping follows the rule:

{circumflex over (R)}≈C(R−μ)+μ  (4)

where μ is the mean of the chrominance values. In one embodiment, 128 isassumed to be the mean for unsigned 8-bit representation of chrominancevalues. This rule for chrominance remapping does not use a brightnesscomponent.

In some embodiments, the two-parameter linear remapping used inEquations 3 and 4 is extended to higher order terms. For example,Equation 5 is a second-order equation that remaps the luminance valuesof R to {circumflex over (R)}:

{circumflex over (R)}≈C ₁ R ² +C ₂ R+B  (5)

Other embodiments use other remapping rules. In one category of suchremapping rules, for non-linear fading, linear mappings are replacedwith non-linear mappings.

C. Estimation of Fading Parameters

Fading estimation is the process of computing fading parameters duringthe encoding process. An encoder such as one shown in the framework(1000) of FIG. 10 may compute brightness (B) and contrast (C) parametersduring the encoding process.

In some embodiments, in order to estimate parameters accurately and in aspeedy manner, the encoder uses the technique (1200) illustrated in FIG.12. In the illustrated technique, only the luminance channel isanalyzed. Alternatively, the encoder includes chrominance in theanalysis when more computational resources are available. For example,the encoder solves for C in Equations 3 and 4 (not just Equation 3) tomake C more robust.

In the embodiment illustrated in FIG. 12, motion in the scene is ignoredduring the fading estimation process. This is based on the observationsthat (a) fades and cross fades typically happen at still or low-motionscenes and (b) the utility of global luminance change compensation inhigh motion scenes is very low. Alternatively, the encoder jointlysolves for fading parameters and motion information. Motion informationis then used to refine the accuracy of fading parameters at the laterstages of the technique (1200) or at some other time. One way to usemotion information is to omit from the fading estimation computationthose portions of the reference frame in which movement is detected.

In various parts of the technique (1200), the absolute error sums ofΣabs(I(n)−R) or Σabs(I(n)−{circumflex over (R)}) serve as metrics fordetermining the existence and parameters of fading. Alternatively, theencoder uses other or additional metrics such as sum of squared errors[“SSE”] or mean squared error [“MSE”] over the same error term, or theencoder uses a different error term.

At various points during the technique (1200), the encoder may end thetechnique (1200) upon satisfaction of an exit condition. FIG. 12 showsseveral exit conditions. For another exit condition (not shown in FIG.12), the encoder checks whether the contrast parameter is close to 1.0(in one implementation, 0.99<C<1.02) at the start or at an intermediatestage of the technique (1200) and, if so, ends the technique (1200).

The encoder begins the process (1200) by downsampling the current frameand the reference frame (1210). In one implementation, the encoderdownsamples by a factor of 4 horizontally and vertically. Alternatively,the encoder downsamples by another factor, or does not downsample atall.

The encoder then computes the absolute error sum Σabs(I_(d)(n)−R_(d))over the lower-resolution versions of the current and reference frames(indicated by the subscript d) (1220). The absolute error sum measuresdifferences in pixel values between the downsampled current frame andthe downsampled reference frame. If the absolute error sum is smallerthan a certain threshold (1230) (e.g., a pre-determined differencemeasure between luminance values for pixels in the downsampled currentand reference frames), the encoder concludes that no fading has occurredand does not perform fading compensation (1235).

Otherwise, the encoder estimates brightness (B) and contrast (C)parameters (1240). First cut estimates for B and C are obtained bymodeling I_(d)(n) in terms of R_(d). In one embodiment, the brightnessand contrast parameters are obtained through linear regression over theentire downsampled frame. In other embodiments, the encoder uses otherforms of statistical analysis such as total least squares, least medianof squares, etc. for more robust analysis. For example, the encoderminimizes the MSE or SSE of the error term I_(d)(n)−R_(d). In somecircumstances, MSE and SSE are not robust, so the encoder also tests theabsolute error sum for the error term. The encoder discards high errorvalues for particular points (which may be due to motion rather thanfading).

The encoder then calculates B_(f) and C_(f) by quantizing anddequantizing B and C (1250). The first cut parameters are quantized anddequantized, giving B_(f) and C_(f), to ensure that they lie within thepermissible range and to test for compliance. In some embodiments, fortypical 8-bit depth imagery, B and C are quantized to 6 bits each. Btakes on integer values from −32 to 31 represented as a signed 5-bitinteger. The quantized value of C, represented as C_(q), varies from0.515625 to 1.484375, in uniform steps of 0.015625 ( 1/64),corresponding to quantized values 1 through 63. Quantization isperformed by rounding B and C to the nearest valid dequantized value andpicking the appropriate bin index.

The encoder then calculates the original bounded absolute error sum(S_(OrgBnd)) and remapped bounded absolute error sum (S_(RmpBnd))(1270). In some embodiments, the encoder calculates the sums using agoodness-of-fit analysis. For a random or pseudorandom set of pixels atthe original resolution, the encoder computes the remapped boundedabsolute error sum Σbabs(I(n)−C_(f)R−B_(f)), where babs(x)=min(abs(x),M) for some bound M. In one implementation, M is a multiple of thequantization parameter of the frame being encoded. The bound M is higherwhen the quantization parameter is coarse, and lower when thequantization parameter is fine. The encoder also accumulates theoriginal bounded absolute error sum Σbabs(I(n)−R). If computationalresources are available, the encoder may compute the bounded error sumsover the entire frames.

Then, based on the relative values of the original and remapped boundedabsolute error sum, the encoder determines whether or not to use fadingcompensation (1280). For example, in some embodiments, the encoder doesnot perform fading compensation unless the remapped bounded absoluteerror sum is less than or equal to some threshold percentage σ of theoriginal bounded absolute error sum. In one implementation, σ=0.95. Ifthe encoder is performing fading compensation, the encoder recomputesthe fading parameters, this time based on a linear regression betweenI(n) and R, but at the full resolution (1290). To save computation time,the encoder can perform the repeated linear regression over the randomor pseudorandom sampling of the frame. Again, the encoder canalternatively use other forms of statistical analysis (e.g., total leastsquares, least median of squares, etc.) for more robust analysis. Whenencoding is done (1295), the process ends.

In one embodiment, the encoder allows a special case in which C=−1 inEquations 3 and 4. The special case is signaled by C_(q)=0 in thebitstream. In this “invert” mode, the reference frame is inverted beforeshifting by B, and the range of B is 193 to 319 in uniform steps of two.

D. Signaling

An encoder such as one shown in the framework (1000) of FIG. 10 sendsfading on/off signaling information as well as fading parameters. Adecoder such as one shown in the framework (1100) of FIG. 11 receivesthe signaling information and fading parameters.

In some embodiments, at the sequence level, the encoder sends one bit toindicate whether global luminance change compensation is enabled for thesequence. The encoder also can signal global luminance change at theframe level and/or signal global luminance change for a portion of aframe, such as an individual block or macroblock in a frame.

Among the frames in a typical video sequence, the occurrence of globalluminance change or fading is rare. It is possible to signal fading (or,equivalently, the absence of fading) by adding a bit. For example, theencoder can signal fading (or, equivalently, the absence of fading) atthe frame level by using one bit. However, it is more economical tosignal fading (or, equivalently, the absence of fading) jointly withother information. For example, the encoder performs frame-levelsignaling using an escape sequence in a variable length code (VLC) tablefor motion mode (i.e., the number and configuration of motion vectors,the sub-pixel interpolation scheme). In such embodiments, the encoderjointly signals the least frequent motion mode and the activation offading compensation.

Let the event F represent the existence of fading and G represent thechoice of the least frequent motion mode for the frame. Let the VLC<MVMODE> represent a motion mode when G is false. This VLC table isdesigned with an escape symbol <ESC> used to signal when F and/or G istrue. Table 1 illustrates the joint coding of F and the motion mode,followed by the fading parameters B and C when fading compensation isactivated.

TABLE 1 Joint coding of least frequent motion mode and fading signal G =false G = true F = false <MVMODE> <ESC> 0 F = true <ESC> 1<MVMODE>[B][C] <ESC> 1 <ESC> [B][C]If global luminance change is activated for a certain frame, another VLC(either <MVMODE> or <ESC>) follows to indicate the motion mode. Then,the parameters are signaled using two 6-bit fixed-length code words forB and C, respectively. Alternatively, the parameters are signaled usingVLCs. When applied to an individual portion of a frame, such as a blockor a macroblock, the encoder can signal the fading parametersindependently with that portion. For example, if fading applies to onlya macroblock of the video frame, the fading information can be signaledby joint entropy codes with macroblock-level information such as thecoded block pattern or transform type.

FIG. 13 is a tree diagram showing a coding arrangement (corresponding toTable 1, for one implementation) where the encoder jointly codes theleast frequent motion mode and the existence of fading. (Details ofcoding for fading parameters are omitted.) The encoder uses VLCs torepresent each of the other motion modes (e.g., the codes “0,” “10,” and“110” represent motion modes 0, 1 and 2, respectively). The encoder usesan escape code (e.g., “111”) followed by a “0” to represent the leastfrequent motion mode when fading is not activated. The encoder uses theescape code followed by a “1” to indicate that fading is activated.

It should be understood that the programs, processes, or methodsdescribed herein are not related or limited to any particular type ofcomputing environment, unless indicated otherwise. Various types ofgeneral purpose or specialized computing environments may be used withor perform operations in according with the teachings described herein.Elements of embodiments shown in software may be implemented in hardwareand vice versa.

In view of the many possible embodiments to which the principles of ourinvention may be applied, we claim as our invention all such embodimentsas may come within the scope and the spirit of the following claims andequivalents thereto.

1. A computer-readable medium storing computer-executable instructionsfor causing a computer system programmed thereby to perform acomputer-implemented method of processing one or more video images, themethod comprising: obtaining fading parameters comprising a scalingparameter and a shifting parameter; performing fading compensation bycomputing plural remapped pixel values for a remapped reference videoimage, wherein the plural remapped pixel values are based at least inpart upon remapping of plural original pixel values used in an originalreference video image in terms of the fading parameters; and performingmotion compensation for at least part of a current video image relativeto the remapped reference video image.
 2. The computer-readable mediumof claim 1 wherein the fading parameters consist of only the scalingparameter and the shifting parameter, and wherein the scaling parameteris a contrast parameter and the shifting parameter is a brightnessparameter.
 3. The computer-readable medium of claim 2 wherein the fadingparameters specify a pixel-wise linear transform comprising: scalingluminance values of the plural original pixel values using the contrastparameter; and shifting the scaled luminance values using the brightnessparameter.
 4. The computer-readable medium of claim 2 wherein thecontrast parameter is greater than approximately 0.5 and less thanapproximately 1.5, and wherein the brightness parameter is greater thanapproximately −32 and less than approximately
 31. 5. Thecomputer-readable medium of claim 2 wherein the fading parametersspecify a linear transform comprising: scaling luminance values of theplural original pixel values using the contrast parameter, wherein thecontrast parameter is −1; and shifting the scaled luminance values usinga modified brightness parameter.
 6. The computer-readable medium ofclaim 1 wherein the scaling parameter is a contrast parameter.
 7. Thecomputer-readable medium of claim 6 wherein the fading parametersspecify a pixel-wise linear transform comprising: scaling chrominancevalues of the plural original pixel values using the contrast parameter.8. The computer-readable medium of claim 1 wherein the fadingcompensation includes multiplying pixel values by the scaling parameterand adding the shifting parameter to the result.
 9. Thecomputer-readable medium of claim 1 wherein the scaling parameter is aweighting parameter and the shifting parameter is an offset parameter.10. The computer-readable medium of claim 1 wherein the method isperformed during video decoding.
 11. The computer-readable medium ofclaim 1 wherein the method is performed during video encoding.
 12. Thecomputer-readable medium of claim 1 wherein the fading parameters arefixed length coded.
 13. The computer-readable medium of claim 1 whereinthe fading parameters are variable length coded.
 14. A computer-readablemedium storing computer-executable instructions for causing a computersystem programmed thereby to perform a computer-implemented method ofprocessing a current video frame in a video sequence, the methodcomprising: obtaining global luminance change compensation parametersfor the current video frame; performing global luminance changecompensation by computing remapped pixel values for a remapped referencevideo frame, wherein the remapped pixel values are based at least inpart upon remapping of original pixel values used in an originalreference video frame in terms of the global luminance changecompensation parameters for the current video frame, wherein the globalluminance change compensation parameters consist of a scaling parameterand a shifting parameter, and wherein the remapped pixel values are forall of the reference video frame; and performing motion compensation forat least part of the current video frame relative to the remappedreference video frame.
 15. The computer-readable medium of claim 14wherein the scaling parameter is a contrast parameter and the shiftingparameter is a brightness parameter.
 16. The computer-readable medium ofclaim 15 wherein the global luminance change parameters specify apixel-wise linear transform comprising: scaling luminance values of theoriginal pixel values using the contrast parameter; and shifting thescaled luminance values using the brightness parameter.
 17. Thecomputer-readable medium of claim 14 wherein the scaling parameter is acontrast parameter.
 18. The computer-readable medium of claim 17 whereinthe global luminance change compensation parameters specify a pixel-wiselinear transform comprising: scaling the chrominance values of theoriginal pixel values using the contrast parameter.
 19. Thecomputer-readable medium of claim 14 wherein the method is performedduring video decoding.
 20. The computer-readable medium of claim 14wherein the method is performed during video encoding. 21-25. (canceled)